Rating prediction for recommendation: Constructing user profiles and item characteristics using backpropagation

Purkaystha, Bishwajit; Datta, Tapos; Islam, Md. Saiful; Marium-E-Jannat,

doi:10.1016/j.asoc.2018.11.018

Cited by 10 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The elements with non-zero values are the known interactions for those users and items. The benefit of forming the interactions as a matrix is that algebraic techniques can easily be applied to it [34]. The CF personalized recommendation is based on the prediction…”

Section: Collaborative Filtering Recommender Systemmentioning

confidence: 99%

Incorporating user rating credibility in recommender systems

Kermany

Zhao

Batsuuri

et al. 2023

Future Generation Computer Systems

View full text Add to dashboard Cite

Section: Collaborative Filtering Recommender Systemmentioning

confidence: 99%

Incorporating user rating credibility in recommender systems

Kermany

Zhao

Batsuuri

et al. 2023

Future Generation Computer Systems

View full text Add to dashboard Cite

“…In the study, further extension of JRL framework is also proposed in which new views (information sources) can be integrated without re-training existing views. Purkaystha et al (2019) proposed a model that learns both user factors and item characteristics and their complex relationships on rating prediction concurrently exploiting user/items identities and ratings using traditional feed-forward neural network (FFNN). It generates user profiles and item properties (without exploiting any demographic information) and then using these constructed features predict the degree of acceptability of an item to a user.…”

Section: Other Modelsmentioning

confidence: 99%

Deep learning techniques for rating prediction: a survey of the state-of-the-art

et al. 2020

View full text Add to dashboard Cite

With the growth of online information, varying personalization drifts and volatile behaviors of internet users, recommender systems are effective tools for information filtering to overcome the information overload problem. Recommender systems utilize rating prediction approaches i.e. predicting the rating that a user will give to a particular item, to generate ranked lists of items according to the preferences of each user in order to make personalized recommendations. Although previous recommendation systems are effective in creating attired recommendations, however, they still suffer from different types of challenges such as accuracy, scalability, cold-start, and data sparsity. In the last few years, deep learning has attained substantial interest in various research areas such as computer vision, speech recognition, and natural language processing. Deep learning based approaches are vigorous in not only performance improvement but also to feature representations learning from the scratch. The impact of deep learning is also prevalent, recently validating its efficacy on information retrieval and recommender systems research. In this study, a comprehensive review of deep learning-based rating prediction approaches is provided to help out new researchers interested in the subject. More concretely, the classification of deep learning-based recommendation/rating prediction models is provided and articulated along with an extensive summary of the state-of-the-art. Lastly, new trends are exposited with new perspectives pertaining to this novel and exciting development of the field.

show abstract

“…Recently, RSs are no longer depending on the rating matrix only for the purpose of producing an accurate personalized recommendation, but they try to make use of various kind of available data on the web. This include the users" demography [1], [2] users and items features [3], [4], tags and time [5], social relation [6], [7] contextual features [8]- [10], and geographic information [11], [12] which are somehow incorporated into the recommendation models for the purpose of improving the performance of the recommendation.…”

Section: Introductionmentioning

confidence: 99%

Tensorized Neural Network for Multi-Aspect Rating-Based Recommendation

2019

IJRTE

View full text Add to dashboard Cite

Generating personalized recommendations is one of the most crucial aspects in Recommender System research area. Most of the researches only focus on the accuracy of recommendation using collaborative filtering that relies on a single overall rating that represents the overall preferences. However, the user may have a different emphasis on different specific aspects that affect the users’ final rating decisions. Therefore, we present a neural network model that utilize multi-aspects ratings using Tensor Factorization to improve the accuracy of personalization, as well as optimizing the dynamic weights of the aspect. To measure the estimated weights for the aspects, we employ the Higher Order Singular Value Decomposition (HOSVD) technique called CANDECOMP/PARAFAC (CP) decomposition that allows for multi-faceted data processing. We then develop the Neural Network with backpropagation error to train the model with different parameter settings and limited computational time. We also use a non-linear activation function in each hidden layer in various settings. The experimental result measured using MAE shows that the proposed model has significantly outperformed the baseline approach in terms of the prediction accuracy. Based on the observation, the performance of rating prediction has been improved by employing the Tensorized Neural Network model and can overcome the problem of local optimum convergence for multi-aspect rating recommendation.

show abstract

Rating prediction for recommendation: Constructing user profiles and item characteristics using backpropagation

Cited by 10 publications

References 19 publications

Incorporating user rating credibility in recommender systems

Incorporating user rating credibility in recommender systems

Deep learning techniques for rating prediction: a survey of the state-of-the-art

Tensorized Neural Network for Multi-Aspect Rating-Based Recommendation

Contact Info

Product

Resources

About